{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "from ggplot import *"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### `scale_color_yhat`\n",
    "`scale_color_yhat` applies Yhat colors to discrete color variables in your ggplots."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<ggplot: (284430245)>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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gBwAAAAwikAMAAAAGEcgBAAAAgwjkAAAAgEEEcgAAAMAgAjkAAABgEIEcAAAAMIhADgAA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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x105ead550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ggplot(mtcars, aes(x='wt', y='mpg', color='factor(cyl)')) + \\\n",
    "    geom_point(size=150) + \\\n",
    "    scale_color_yhat()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
